Comparative Study of Genetic Algorithms for the School Timetabling Problem

The School Timetabling Problem (STP) aims to allocate subjects to specific times for viable planning while avoiding scheduling conflicts between professors, classes, and other resources. We observed this problem in a higher education institution and, to solve it, implemented the genetic algorithm (GA) and variants RKGA (Random-Key Genetic Algorithm) and BRKGA (Biased Random-Key Genetic Algorithm). Different parameterizations were experimented with using real data. We compare the developed methods considering criteria such as the quality of the solutions and execution time. The results show that BRKGA obtained feasible schedules and better quality solutions than RKGA in 76.67% of the cases and all cases compared to GA.